Fault detection of rotating machinery based on wavelet transform and improved deep neural network

被引：0

作者：

Cui, Mingliang ^{[1
]}

Wang, Youqing ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China

来源：

PROCEEDINGS OF 2020 IEEE 9TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS'20) | 2020年

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

Gearbox; Fault detection; Wavelet analysis; Improved CNN-SVM; DIAGNOSIS;

D O I：

10.1109/ddcls49620.2020.9275102

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the operation of wind turbine, gearbox faults are very common. It is very important to detect the fault effectively to ensure the safe and reliable operation of wind turbine. In this study, the wavelet analysis method is combined with an improved convolutional neural network and support vector machine (CNN-SVM), and the proposed method is applied to the fault detection and classification of the gearbox in the wind power generation equipment in the laboratory. The experimental results show that the proposed method achieves super classification result.

引用

页码：449 / 454

页数：6

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